764 lines
32 KiB
Python
764 lines
32 KiB
Python
# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Testing suite for the PyTorch Speech2Text model. """
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import copy
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import inspect
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import os
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import tempfile
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import unittest
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from transformers import Speech2TextConfig
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from transformers.file_utils import cached_property
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from transformers.testing_utils import (
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is_torch_available,
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require_sentencepiece,
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require_tokenizers,
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require_torch,
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require_torchaudio,
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slow,
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torch_device,
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)
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from .test_configuration_common import ConfigTester
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from .test_generation_utils import GenerationTesterMixin
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from .test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
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if is_torch_available():
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import torch
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from transformers import Speech2TextForConditionalGeneration, Speech2TextModel, Speech2TextProcessor
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from transformers.models.speech_to_text.modeling_speech_to_text import Speech2TextDecoder, Speech2TextEncoder
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def prepare_speech_to_text_inputs_dict(
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config,
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input_features,
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decoder_input_ids,
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attention_mask=None,
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decoder_attention_mask=None,
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head_mask=None,
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decoder_head_mask=None,
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cross_attn_head_mask=None,
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):
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if attention_mask is None:
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attention_mask = input_features.ne(0)
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if decoder_attention_mask is None:
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decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id)
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if head_mask is None:
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head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
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if decoder_head_mask is None:
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decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
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if cross_attn_head_mask is None:
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cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
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return {
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# "input_ids": input_features,
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"input_features": input_features,
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"decoder_input_ids": decoder_input_ids,
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"attention_mask": attention_mask,
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"decoder_attention_mask": attention_mask,
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"head_mask": head_mask,
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"decoder_head_mask": decoder_head_mask,
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"cross_attn_head_mask": cross_attn_head_mask,
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}
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@require_torch
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class Speech2TextModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_labels=False,
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vocab_size=99,
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hidden_size=16,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=4,
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num_conv_layers=2,
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conv_kernel_sizes=(5, 5),
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conv_channels=32,
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input_feat_per_channel=24,
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input_channels=1,
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hidden_act="relu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=20,
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max_source_positions=20,
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max_target_positions=20,
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eos_token_id=2,
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pad_token_id=1,
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bos_token_id=0,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.num_conv_layers = num_conv_layers
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self.conv_kernel_sizes = conv_kernel_sizes
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self.conv_channels = conv_channels
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self.input_feat_per_channel = input_feat_per_channel
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self.input_channels = input_channels
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.max_source_positions = max_source_positions
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self.max_target_positions = max_target_positions
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self.eos_token_id = eos_token_id
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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def prepare_config_and_inputs(self):
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input_features = floats_tensor(
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[self.batch_size, self.seq_length, self.input_feat_per_channel], self.vocab_size
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)
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attention_mask = torch.ones([self.batch_size, self.seq_length], dtype=torch.long, device=torch_device)
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decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(2)
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config = self.get_config()
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inputs_dict = prepare_speech_to_text_inputs_dict(
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config,
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input_features=input_features,
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decoder_input_ids=decoder_input_ids,
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attention_mask=attention_mask,
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)
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return config, inputs_dict
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def get_config(self):
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return Speech2TextConfig(
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vocab_size=self.vocab_size,
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d_model=self.hidden_size,
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encoder_layers=self.num_hidden_layers,
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decoder_layers=self.num_hidden_layers,
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encoder_attention_heads=self.num_attention_heads,
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decoder_attention_heads=self.num_attention_heads,
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encoder_ffn_dim=self.intermediate_size,
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decoder_ffn_dim=self.intermediate_size,
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num_conv_layers=self.num_conv_layers,
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conv_kernel_sizes=self.conv_kernel_sizes,
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conv_channels=self.conv_channels,
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input_feat_per_channel=self.input_feat_per_channel,
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input_channels=self.input_channels,
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dropout=self.hidden_dropout_prob,
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attention_dropout=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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max_source_positions=self.max_source_positions,
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max_target_positions=self.max_target_positions,
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eos_token_id=self.eos_token_id,
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bos_token_id=self.bos_token_id,
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pad_token_id=self.pad_token_id,
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)
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def prepare_config_and_inputs_for_common(self):
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config, inputs_dict = self.prepare_config_and_inputs()
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return config, inputs_dict
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def get_subsampled_output_lengths(self, input_lengths):
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"""
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Computes the output length of the convolutional layers
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"""
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for i in range(self.num_conv_layers):
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input_lengths = (input_lengths - 1) // 2 + 1
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return input_lengths
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def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
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model = Speech2TextModel(config=config).get_decoder().to(torch_device).eval()
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input_ids = inputs_dict["decoder_input_ids"]
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attention_mask = inputs_dict["decoder_attention_mask"]
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# first forward pass
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outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
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output, past_key_values = outputs.to_tuple()
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# create hypothetical multiple next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size).clamp(2)
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next_attn_mask = ids_tensor((self.batch_size, 3), 2)
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# append to next input_ids and
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next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
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output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
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output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
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"last_hidden_state"
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]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
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output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
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self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
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# test that outputs are equal for slice
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self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2))
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def check_encoder_decoder_model_standalone(self, config, inputs_dict):
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model = Speech2TextModel(config=config).to(torch_device).eval()
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outputs = model(**inputs_dict)
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encoder_last_hidden_state = outputs.encoder_last_hidden_state
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last_hidden_state = outputs.last_hidden_state
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with tempfile.TemporaryDirectory() as tmpdirname:
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encoder = model.get_encoder()
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encoder.save_pretrained(tmpdirname)
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encoder = Speech2TextEncoder.from_pretrained(tmpdirname).to(torch_device)
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encoder_last_hidden_state_2 = encoder(
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inputs_dict["input_features"], attention_mask=inputs_dict["attention_mask"]
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)[0]
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self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
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with tempfile.TemporaryDirectory() as tmpdirname:
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decoder = model.get_decoder()
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decoder.save_pretrained(tmpdirname)
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decoder = Speech2TextDecoder.from_pretrained(tmpdirname).to(torch_device)
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last_hidden_state_2 = decoder(
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input_ids=inputs_dict["decoder_input_ids"],
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attention_mask=inputs_dict["decoder_attention_mask"],
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encoder_hidden_states=encoder_last_hidden_state,
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encoder_attention_mask=inputs_dict["attention_mask"],
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)[0]
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self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)
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@require_torch
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class Speech2TextModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
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all_model_classes = (Speech2TextModel, Speech2TextForConditionalGeneration) if is_torch_available() else ()
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all_generative_model_classes = (Speech2TextForConditionalGeneration,) if is_torch_available() else ()
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is_encoder_decoder = True
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test_pruning = False
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test_missing_keys = False
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test_torchscript = True
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input_name = "input_features"
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def setUp(self):
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self.model_tester = Speech2TextModelTester(self)
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self.config_tester = ConfigTester(self, config_class=Speech2TextConfig)
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self.maxDiff = 3000
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_save_load_strict(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs()
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for model_class in self.all_model_classes:
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model = model_class(config)
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
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self.assertEqual(info["missing_keys"], [])
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def test_decoder_model_past_with_large_inputs(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
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def test_encoder_decoder_model_standalone(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
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self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
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def test_inputs_embeds(self):
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pass
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# training is not supported yet
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def test_training(self):
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pass
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def test_training_gradient_checkpointing(self):
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pass
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def test_generate_fp16(self):
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config, input_dict = self.model_tester.prepare_config_and_inputs()
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input_features = input_dict["input_features"]
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attention_mask = input_dict["attention_mask"]
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model = Speech2TextForConditionalGeneration(config).eval().to(torch_device)
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if torch_device == "cuda":
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input_features = input_features.half()
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model.half()
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model.generate(input_features, attention_mask=attention_mask)
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model.generate(input_features, num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
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def test_forward_signature(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = [
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"input_features",
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"attention_mask",
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"decoder_input_ids",
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"decoder_attention_mask",
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]
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expected_arg_names.extend(
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["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"]
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if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names
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else ["encoder_outputs"]
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)
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self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
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def test_hidden_states_output(self):
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def check_hidden_states_output(inputs_dict, config, model_class):
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
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expected_num_layers = getattr(
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self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
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)
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self.assertEqual(len(hidden_states), expected_num_layers)
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if hasattr(self.model_tester, "encoder_seq_length"):
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seq_length = self.model_tester.encoder_seq_length
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else:
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seq_length = self.model_tester.seq_length
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subsampled_seq_length = model._get_subsampled_output_lengths(seq_length)
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self.assertListEqual(
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list(hidden_states[0].shape[-2:]),
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[subsampled_seq_length, self.model_tester.hidden_size],
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)
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if config.is_encoder_decoder:
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hidden_states = outputs.decoder_hidden_states
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self.assertIsInstance(hidden_states, (list, tuple))
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self.assertEqual(len(hidden_states), expected_num_layers)
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seq_len = getattr(self.model_tester, "seq_length", None)
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decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
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self.assertListEqual(
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list(hidden_states[0].shape[-2:]),
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[decoder_seq_length, self.model_tester.hidden_size],
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)
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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inputs_dict["output_hidden_states"] = True
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check_hidden_states_output(inputs_dict, config, model_class)
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# check that output_hidden_states also work using config
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del inputs_dict["output_hidden_states"]
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config.output_hidden_states = True
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check_hidden_states_output(inputs_dict, config, model_class)
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def test_attention_outputs(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.return_dict = True
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seq_len = getattr(self.model_tester, "seq_length", None)
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decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
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encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
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decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
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encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
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for model_class in self.all_model_classes:
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = False
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config.return_dict = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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subsampled_encoder_seq_length = model._get_subsampled_output_lengths(encoder_seq_length)
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subsampled_encoder_key_length = model._get_subsampled_output_lengths(encoder_key_length)
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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# check that output_attentions also work using config
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del inputs_dict["output_attentions"]
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config.output_attentions = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length],
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)
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out_len = len(outputs)
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correct_outlen = 5
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# loss is at first position
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if "labels" in inputs_dict:
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correct_outlen += 1 # loss is added to beginning
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if "past_key_values" in outputs:
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correct_outlen += 1 # past_key_values have been returned
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self.assertEqual(out_len, correct_outlen)
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# decoder attentions
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decoder_attentions = outputs.decoder_attentions
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self.assertIsInstance(decoder_attentions, (list, tuple))
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self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(decoder_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
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)
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# cross attentions
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cross_attentions = outputs.cross_attentions
|
|
self.assertIsInstance(cross_attentions, (list, tuple))
|
|
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
|
|
self.assertListEqual(
|
|
list(cross_attentions[0].shape[-3:]),
|
|
[
|
|
self.model_tester.num_attention_heads,
|
|
decoder_seq_length,
|
|
subsampled_encoder_key_length,
|
|
],
|
|
)
|
|
|
|
# Check attention is always last and order is fine
|
|
inputs_dict["output_attentions"] = True
|
|
inputs_dict["output_hidden_states"] = True
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
added_hidden_states = 2
|
|
self.assertEqual(out_len + added_hidden_states, len(outputs))
|
|
|
|
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
|
|
|
|
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
|
|
self.assertListEqual(
|
|
list(self_attentions[0].shape[-3:]),
|
|
[self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length],
|
|
)
|
|
|
|
def test_resize_tokens_embeddings(self):
|
|
(
|
|
original_config,
|
|
inputs_dict,
|
|
) = self.model_tester.prepare_config_and_inputs_for_common()
|
|
if not self.test_resize_embeddings:
|
|
return
|
|
|
|
for model_class in self.all_model_classes:
|
|
config = copy.deepcopy(original_config)
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
|
|
if self.model_tester.is_training is False:
|
|
model.eval()
|
|
|
|
model_vocab_size = config.vocab_size
|
|
# Retrieve the embeddings and clone theme
|
|
model_embed = model.resize_token_embeddings(model_vocab_size)
|
|
cloned_embeddings = model_embed.weight.clone()
|
|
|
|
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
|
|
model_embed = model.resize_token_embeddings(model_vocab_size + 10)
|
|
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
|
|
# Check that it actually resizes the embeddings matrix
|
|
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
|
|
# Check that the model can still do a forward pass successfully (every parameter should be resized)
|
|
model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
|
|
model_embed = model.resize_token_embeddings(model_vocab_size - 15)
|
|
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
|
|
# Check that it actually resizes the embeddings matrix
|
|
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
|
|
|
|
# make sure that decoder_input_ids are resized
|
|
if "decoder_input_ids" in inputs_dict:
|
|
inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
|
|
model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
# Check that adding and removing tokens has not modified the first part of the embedding matrix.
|
|
models_equal = True
|
|
for p1, p2 in zip(cloned_embeddings, model_embed.weight):
|
|
if p1.data.ne(p2.data).sum() > 0:
|
|
models_equal = False
|
|
|
|
self.assertTrue(models_equal)
|
|
|
|
def test_resize_embeddings_untied(self):
|
|
(
|
|
original_config,
|
|
inputs_dict,
|
|
) = self.model_tester.prepare_config_and_inputs_for_common()
|
|
if not self.test_resize_embeddings:
|
|
return
|
|
|
|
original_config.tie_word_embeddings = False
|
|
|
|
# if model cannot untied embeddings -> leave test
|
|
if original_config.tie_word_embeddings:
|
|
return
|
|
|
|
for model_class in self.all_model_classes:
|
|
config = copy.deepcopy(original_config)
|
|
model = model_class(config).to(torch_device)
|
|
|
|
# if no output embeddings -> leave test
|
|
if model.get_output_embeddings() is None:
|
|
continue
|
|
|
|
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
|
|
model_vocab_size = config.vocab_size
|
|
model.resize_token_embeddings(model_vocab_size + 10)
|
|
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
|
|
output_embeds = model.get_output_embeddings()
|
|
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10)
|
|
# Check bias if present
|
|
if output_embeds.bias is not None:
|
|
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10)
|
|
# Check that the model can still do a forward pass successfully (every parameter should be resized)
|
|
model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
|
|
model.resize_token_embeddings(model_vocab_size - 15)
|
|
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
|
|
# Check that it actually resizes the embeddings matrix
|
|
output_embeds = model.get_output_embeddings()
|
|
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15)
|
|
# Check bias if present
|
|
if output_embeds.bias is not None:
|
|
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15)
|
|
# Check that the model can still do a forward pass successfully (every parameter should be resized)
|
|
if "decoder_input_ids" in inputs_dict:
|
|
inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
|
|
# Check that the model can still do a forward pass successfully (every parameter should be resized)
|
|
model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
def test_generate_without_input_ids(self):
|
|
pass
|
|
|
|
@staticmethod
|
|
def _get_encoder_outputs(
|
|
model, input_ids, attention_mask, output_attentions=None, output_hidden_states=None, num_interleave=1
|
|
):
|
|
encoder = model.get_encoder()
|
|
encoder_outputs = encoder(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
)
|
|
encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.repeat_interleave(
|
|
num_interleave, dim=0
|
|
)
|
|
input_ids = input_ids[:, :, 0]
|
|
input_ids = torch.zeros_like(input_ids[:, :1], dtype=torch.long) + model._get_decoder_start_token_id()
|
|
attention_mask = None
|
|
return encoder_outputs, input_ids, attention_mask
|
|
|
|
def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1):
|
|
batch_size, seq_length = input_ids.shape[:2]
|
|
subsampled_seq_length = self.model_tester.get_subsampled_output_lengths(seq_length)
|
|
num_sequences_in_output = batch_size * num_return_sequences
|
|
gen_len = (
|
|
output.sequences.shape[-1] - 1 if config.is_encoder_decoder else output.sequences.shape[-1] - seq_length
|
|
)
|
|
|
|
# scores
|
|
self._check_scores(num_sequences_in_output, output.scores, length=gen_len, config=config)
|
|
|
|
# Attentions
|
|
# encoder
|
|
self._check_encoder_attention_for_generate(
|
|
output.encoder_attentions, batch_size, config, subsampled_seq_length
|
|
)
|
|
# decoder
|
|
self._check_attentions_for_generate(
|
|
num_sequences_in_output,
|
|
output.decoder_attentions,
|
|
min_length=1,
|
|
max_length=output.sequences.shape[-1],
|
|
config=config,
|
|
use_cache=use_cache,
|
|
)
|
|
|
|
# Hidden States
|
|
# encoder
|
|
self._check_encoder_hidden_states_for_generate(
|
|
output.encoder_hidden_states, batch_size, config, subsampled_seq_length
|
|
)
|
|
|
|
# decoder
|
|
self._check_hidden_states_for_generate(
|
|
num_sequences_in_output,
|
|
output.decoder_hidden_states,
|
|
min_length=1,
|
|
max_length=output.sequences.shape[-1],
|
|
config=config,
|
|
use_cache=use_cache,
|
|
)
|
|
|
|
def _create_and_check_torchscript(self, config, inputs_dict):
|
|
if not self.test_torchscript:
|
|
return
|
|
|
|
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
|
|
configs_no_init.torchscript = True
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config=configs_no_init)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
|
|
try:
|
|
model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
|
|
input_features = inputs["input_features"]
|
|
attention_mask = inputs["attention_mask"]
|
|
decoder_input_ids = inputs["decoder_input_ids"]
|
|
decoder_attention_mask = inputs["decoder_attention_mask"]
|
|
traced_model = torch.jit.trace(
|
|
model, (input_features, attention_mask, decoder_input_ids, decoder_attention_mask)
|
|
)
|
|
except RuntimeError:
|
|
self.fail("Couldn't trace module.")
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
|
|
|
|
try:
|
|
torch.jit.save(traced_model, pt_file_name)
|
|
except Exception:
|
|
self.fail("Couldn't save module.")
|
|
|
|
try:
|
|
loaded_model = torch.jit.load(pt_file_name)
|
|
except Exception:
|
|
self.fail("Couldn't load module.")
|
|
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
loaded_model.to(torch_device)
|
|
loaded_model.eval()
|
|
|
|
model_state_dict = model.state_dict()
|
|
loaded_model_state_dict = loaded_model.state_dict()
|
|
|
|
self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
|
|
|
|
models_equal = True
|
|
for layer_name, p1 in model_state_dict.items():
|
|
p2 = loaded_model_state_dict[layer_name]
|
|
if p1.data.ne(p2.data).sum() > 0:
|
|
models_equal = False
|
|
|
|
self.assertTrue(models_equal)
|
|
|
|
|
|
@require_torch
|
|
@require_torchaudio
|
|
@require_sentencepiece
|
|
@require_tokenizers
|
|
@slow
|
|
class Speech2TextModelIntegrationTests(unittest.TestCase):
|
|
@cached_property
|
|
def default_processor(self):
|
|
return Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr")
|
|
|
|
def _load_datasamples(self, num_samples):
|
|
from datasets import load_dataset
|
|
|
|
import soundfile as sf
|
|
|
|
# map files to raw
|
|
def map_to_array(batch):
|
|
speech, _ = sf.read(batch["file"])
|
|
batch["speech"] = speech
|
|
return batch
|
|
|
|
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
|
|
ds = ds.select(range(num_samples)).map(map_to_array)
|
|
|
|
return ds["speech"][:num_samples]
|
|
|
|
def test_generation_librispeech(self):
|
|
model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
|
|
model.to(torch_device)
|
|
processor = self.default_processor
|
|
|
|
input_speech = self._load_datasamples(1)
|
|
|
|
input_features = processor(input_speech, return_tensors="pt").input_features.to(torch_device)
|
|
|
|
generated_ids = model.generate(input_features)
|
|
generated_transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)
|
|
|
|
EXPECTED_TRANSCRIPTIONS = ["a man said to the universe sir i exist"]
|
|
self.assertListEqual(generated_transcript, EXPECTED_TRANSCRIPTIONS)
|
|
|
|
def test_generation_librispeech_batched(self):
|
|
model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")
|
|
model.to(torch_device)
|
|
processor = self.default_processor
|
|
|
|
input_speech = self._load_datasamples(4)
|
|
|
|
inputs = processor(input_speech, return_tensors="pt", padding=True)
|
|
|
|
input_features = inputs.input_features.to(torch_device)
|
|
attention_mask = inputs.attention_mask.to(torch_device)
|
|
|
|
generated_ids = model.generate(input_features, attention_mask=attention_mask)
|
|
generated_transcripts = processor.batch_decode(generated_ids, skip_special_tokens=True)
|
|
|
|
EXPECTED_TRANSCRIPTIONS = [
|
|
"a man said to the universe sir i exist",
|
|
"sweat covered brion's body trickling into the titleing cloth that was the only garment he wore",
|
|
"the cut on his chest still dripping blood the ache of his overstrained eyes even the soaring arena around him with the thousands of spectators were trivialities not worth thinking about",
|
|
"his instant of panic was followed by a small sharp blow high on his chest",
|
|
]
|
|
|
|
self.assertListEqual(generated_transcripts, EXPECTED_TRANSCRIPTIONS)
|